Hardware / Cloud Requirements
Requirements for a single instance/VM
For simplicity and cost-effectiveness, you can run FormX.ai on a single server/instance; However, a single server cannot train new extractors; it can only run pre-built extractors or extractors trained on the FormX.ai SaaS platform.
Minimum Specification:
- 16 Core Intel CPU (each core must be at least 2.6Ghz or faster)
- 32GB RAM
- 120GB SSD
- GPU is not required
Requirements for a cluster
FormX.ai deploys on the Kubernetes cluster by default. Here are the minimum specifications:
Purposes | Number of Instances | Specifications |
---|---|---|
API / Extraction Workers | 3 VMs (minimal for Kubernetes) | 8 vCPU 16GB RAM 30GB SSD |
Database (PostgreSQL) | 1 VMs (require at least 2 for High Availability) | 4 vCPU 8GB RAM 64GB SSD |
For Training Pipeline Only ML Workers for dataset generation | 3 VMs | 8 vCPU 16GB RAM 100GB+ SSD |
For Training PIpeline Only ML Trainer for model training | 1 VM (more for parallel training) | 8 vCPU 16GB RAM 100GB+ SSD GPU: P100 or better 16GB+ vRAM |
For new Transformer Model Only Extraction Workers with GPU | 1 VM | 4 vCPU 15GB RAM 100GB SSD GPU: T4 or better |
Cloud Resources Inventories
For a typical Cloud Deployment, here are the list of Cloud Resources Required:
Inventory | Purposes | Related Cloud Products |
---|---|---|
Kubernetes | Run the applications, workers, trainers | GCP GKE Azure AKS AWS EKS |
Database | Store the configs, audit logs, temporarily result for async requests | GCP Cloud SQL for PostgreSQL Azure Database for PostgreSQL AWS RDS for PostgreSQL |
Image Storage | Storage of the images for training (optional) | Google Cloud Storage Azure Blob Storage AWS S3 |
OCR | OCR | Google Vision API Azure OCR |
Other Software Components | Redis: Cache authentication tokens Authgear: For authentication | Using some pods on the k8s cluster |
Updated over 1 year ago